Assistant Professor Academic Level 12 7th Pay CPC
Symbiosis International Deemed University
Dr. Saikat Gochhait teaches at Symbiosis Institute of Digital & Telecom Management, Symbiosis International Deemed University Pune, India and Neurosciences Research Institute-Samara State Medical University, Russia. He is Ph.D and Post-Doctoral Fellow from the UEx, Spain and National Dong Hwa University, Taiwan. He was Awarded DITA and MOFA Fellowship in 2017 and 2018. His research publication with foreign authors is indexed in Scopus, ABDC, and Web of Science. He is a Senior IEEE member.
Post Doctoral Fellow - Uex, Spain
Post Doctoral Fellow - National Dong Hwa University, Taiwan
PhD - Sambalpur University
Selvakumar Thirumalaisamy, Kamaleshwar Thangavilou, Hariharan Rajadurai, Oumaima Saidani, Nazik Alturki, Sandeep kumar Mathivanan, Prabhu Jayagopal, and Saikat Gochhait MDPI AG
Breast cancer is the second leading cause of mortality among women. Early and accurate detection plays a crucial role in lowering its mortality rate. Timely detection and classification of breast cancer enable the most effective treatment. Convolutional neural networks (CNNs) have significantly improved the accuracy of tumor detection and classification in medical imaging compared to traditional methods. This study proposes a comprehensive classification technique for identifying breast cancer, utilizing a synthesized CNN, an enhanced optimization algorithm, and transfer learning. The primary goal is to assist radiologists in rapidly identifying anomalies. To overcome inherent limitations, we modified the Ant Colony Optimization (ACO) technique with opposition-based learning (OBL). The Enhanced Ant Colony Optimization (EACO) methodology was then employed to determine the optimal hyperparameter values for the CNN architecture. Our proposed framework combines the Residual Network-101 (ResNet101) CNN architecture with the EACO algorithm, resulting in a new model dubbed EACO–ResNet101. Experimental analysis was conducted on the MIAS and DDSM (CBIS-DDSM) mammographic datasets. Compared to conventional methods, our proposed model achieved an impressive accuracy of 98.63%, sensitivity of 98.76%, and specificity of 98.89% on the CBIS-DDSM dataset. On the MIAS dataset, the proposed model achieved a classification accuracy of 99.15%, a sensitivity of 97.86%, and a specificity of 98.88%. These results demonstrate the superiority of the proposed EACO–ResNet101 over current methodologies.
Sujata Roy, Jeyalakshmi Jeyabalan, Saikat Gochhait, Poonkuzhali Sugumaran, and M. Michael Gromiha International Information and Engineering Technology Association
Igor Shirolapov, Alexander Zakharov, Saikat Gochhait, Vasiliy Pyatin, Mariya Sergeeva, Natalia Romanchuk, Yuliya Komarova, Vladimir Kalinin, Olga Pavlova, and Elena Khivintseva World Scientific and Engineering Academy and Society (WSEAS)
Background: In the last decade, the concept of the Glymphatic system as a complexly organized perivascular transport has been formed, it “connects” the cerebrospinal fluid with the lymphatic vessels of the meninges through the extracellular space of the brain. The exact molecular mechanisms of the functioning of the glymphatic pathway have not been fully characterized, but its key role in the cerebral clearance of metabolites and neurotoxic substances is noted. Neurodegenerative diseases affect millions of people around the world, and the most common pathologies from this heterogeneous group of diseases are Alzheimer's disease and Parkinson's disease. Their pathogenesis is based on abnormal protein aggregation, formation of neurofibrillary insoluble structures, and inefficient removal of neurotoxic metabolites. Aim: This article reviewed the evidence linking glymphatic system dysfunction and the development of human neurodegenerative diseases, and noted the key role of aquaporin-4 in the clearance of metabolites from the brain. Setting and Design: The actual sources of data were compiled and reviewed from PubMed, Scopus, and Web of Sciences from 2012 to 2023. Result and Discussion: Glial-dependent perivascular transport promotes the clearance of interstitial solutes, including beta-amyloid, synuclein, and tau protein, from the parenchymal extracellular space of the brain in normal and pathological conditions. An increase in the proportion of metabolites and pathological proteins in the dysfunction of the glymphatic pathway enhances the progression of cognitive impairment and neurodegenerative processes. In turn, the aging process, oxidative stress, and neuroinflammation in Alzheimer's disease and Parkinson's disease contribute to reactive astrogliosis and may impair glymphatic clearance. Conclusion: This review describes in detail the features of the glymphatic system and discusses that its dysfunction plays a fundamental significance in the pathological accumulation of metabolites during the progression of neurodegeneration and neuroinflammation. Understanding these processes will make it possible to take new steps in the prevention and treatment of neurodegenerative diseases.
Saikat Gochhait, Miriam Cano Rubio, Rocío Martínez Jiménez, and Sabiha Fazalbhoy Inderscience Publishers
Saikat Gochhait and Manisha Paliwal IEEE
Developing a GIOT-enabled surveillance cobot is the objective of this study. The cobot transmits real-time video footage from a preset environment to a base control station via internet or Wi-Fi. In this methodology, the cobot is controlled in real time by a human controller, who uses the data to operate the cobot. The cobot is small and independent, and it transmits data wirelessly. An application that monitors and controls a cobot's movements using a wireless network and a Raspberry Pi board can help detect and monitor terrorist attacks around the world.
Saikat Gochhait Inderscience Publishers
Aritra Mitra, Saikat Gochhait, Ahmed J. Obaid, and Mohammed Ayad Alkhafaji IEEE
Since revolutionary digitization has taken hold in all industries and companies, the excessive growth of data is overtaking the world around us. With this explosion of data comes an increased responsibility to protect it from external threats, exploitation and misuse of information. The healthcare industry is expanding its horizons with the latest cutting-edge technologies such as robotic process automation, cloud transformation and digitization, generating several zettabytes of data every year. With this excessive data growth, the responsibility to protect the data from external threats, exploitation and information misuse is also increasing. The steep rise in data breaches, disclosure of important public and corporate data, fraudulent activities such as threatening phone calls, false insurance claims, and even illegal monetary claims have rocked the world. This in turn increases the urgency and need for an advanced, standardized data protection strategy. In this research study, the Scopus database has been used as a source for a bibliometric analysis to discuss recent research activities on big data protection. The expected outcome of this research is a broader understanding of how organizations operating in the healthcare sector are addressing overall data management by shaping existing organizational policies and adapting new security standards.
Mayuri Puri and Saikat Gochhait IEEE
Cyberattacks are used to steal money, data, or intellectual property, but the goal is increasingly to produce overt disruption or political influence. Healthcare is more vulnerable to cyberattacks than other industries due to inherent weaknesses in its security posture. In addition to medical equipment and other systems connected to IT networks, cybersecurity threats and vulnerabilities can pose a threat to the confidentiality, resilience, and veracity of those systems. As a result of the rich supply of valuable data, Healthcare makes a good target for cybercriminals. Additionally, while Cybersecurity is critical for patient safety, it has an unreliable track record. Breach of infrastructure has resulted in millions of health records being stolen, potentially putting patients' lives at risk. This necessitates the integration of Cybersecurity into patient safety. Before these attacks, many security experts struggled to persuade corporate executives of the necessity of cyber security; significantly, a great deal can be gained, in the long run, from risk mitigation, through both cost savings and reputation protection. A holistic solution to prioritizing Cybersecurity in the healthcare business necessitates cultural transformations, enhanced leadership communication, and changes in how practitioners conduct their roles in the clinical setting.
Harsh Verma and Saikat Gochhait IEEE
The primary purpose of Spatio-Temporal Game Analytics (STGA) is to be the primary tool for the business models that are used in the gaming industry for enhancing the player experience to next level. Due to the worldwide expansion in the gaming industry business intelligence and game analytics are being frequently deployed in the value chain of the industry. However, it is still necessary to conduct relevant studies for precise analysis and classifications. With the focus towards spatio-temporal game analytics and Business Intelligence (BI) applications in the gaming industry, the research paper delivers a comprehensive literature review of all the analytics and BI applications that can be applicable to the industry.In this study, five crucial concerns are looked at and explored in the recent literature reviews. The traditional game value chain has been explored which could easily incorporate game analytics into the system for enhancement. Secondly, the main objective behind the use of analytics in the video game industry was determined. Third, we described the issues and irregularities that game analytics can address in the gaming industry. Fourth, we offered multiple algorithms that can be used for predictions in the gaming industry. Finally, this study draws attention to the topics that have already been covered in various literature reviews but still need more research investigation to satisfy the analysis. On the basis of the categories identified after the mapping and analysis of the reviews, certain limitations associated with the game analytic have been addressed and also future study areas that can be added to the research have also been identified and mentioned as future scope areas.
Sweta Mishra and Saikat Gochhait IEEE
The rapid digital transformation across industries, including manufacturing, has created significant blind spots for organizations when it comes to security. The threat surface grows as businesses engage more in automation, scale their operations, and integrate IoT, and many security teams struggle to stay up. However, in the age of digitalization, the emphasis must move from protecting network perimeters to protecting data that is dispersed across systems, devices, and the cloud. Organizations must maintain a strong asset management process, be resilient to cyber risks, and generate business value by being ahead of the curve when it comes to managing cyber threats.
Isshita Paliwal and Saikat Gochhait IEEE
Human Activity Recognition(HAR) is used in many applications, such as surveillance, anti-terrorists, anti-crime securities, medical, life logging, and assistance. Besides its positive effect on well-being, the recognition of human activity has many applications. One of the key research topics in the fields of computer vision and machine learning is the human capacity for activity recognition. Pose estimation and categorization algorithms, which are now available for use on pictures or video input, it is now possible to gather and store information on several elements of human mobility in a free environment. A hierarchical structure is inherent in human activities, which can be categorized into three levels based on the nature of the action. A typical example of a movement happens when one walks, talks, stands, or sits indoors, which are everyday indoor activities. In addition, they may be more focused on activities such as those performed in kitchens or factories. However, A major challenge for activity recognition is the diversity of methods used by individuals. As a technology assistive for eldercare and healthcare, it is expected to be used mainly with the Internet of Things (IoT). The paper has demonstrated that it follows a hierarchy of analytical approaches toward the issue in a clearly defined form, and the paper showed numerous strategies that have been a part of various other studies in the field. Even though a notable amount of progress has been observed in this critical area, there is still space for further improvements in the subject topic, particularly when it comes to applying cutting-edge categorization algorithms to a variety of problems. In order to deal with these challenges, Pose estimation and classification algorithms have been used to evaluate data collection and discover human activities accurately. This study also examines the activities performed by the user during Video/Image input.
Saikat Gochhait and Deepak Sharma Yayasan Riset dan Pengembangan Intelektual
Forecasting load is an integral part of the planning, operation, and control of power systems. This paper is part of a research effort aimed at developing better energy demand forecasting models for load dispatch centers (LDCs) in Indian states as part of an ambitious project utilizing artificial intelligence-based load forecasting models. In this paper, we present a half hourly load forecasting method for the energy management system of the project that will be used at 33 /11 kV and 0.415 kV substations with good accuracy. The paper uses the half-hourly load consumption dataset collected from MSEDCL for Maharashtra from July 1, 2020 through August 31, 2022. This paper evaluates 24 regression model-based half hourly based load forecasting algorithms for ALE PHATA load based on the load consumption dataset and the collected meteorological dataset. The 24 models in MATLAB Regression belong to five types of regression models: Linear Regression, Regression Trees, Support Vector Machines (SVM), Gaussian Process Regression (GPR), Ensemble of Trees, and Neural Networks. As a consequence of their nonparametric kernel-based probabilistic nature, the GPR family of models demonstrates the best load forecasting performance. Least squares estimation was used to determine the regression coefficients. There is a direct correlation between load in an electrical power system and temperature, due point, and seasons, as well as a correlation between load and previous load consumption. Therefore, the input variables are Wet Bulb Temperature at 2 Meters (C), Dew/Frost Point at 2 Meters (C), Temperature at 2 Meters (C), Relative Humidity at 2 Meters (%), Specific Humidity at 2 Meters (g/kg) and Wind Speed at 10 Meters (m/s). The mean absolute percentage error and the R squared are used to validate or verify the accuracy of the model, which is shown in the results section. Based on this study, two GPR models are recommended for load forecasting, the Rational Quadratic GPR and the Exponential GPR and Exponential GPR as final model.
Olga Maslova, Tatiana Vladimirova, Arseny Videnin, Saikat Gochhait, and Vasily Pyatin Frontiers Media SA
PurposeThis experimental study was conducted during the post-COVID-19 period to investigate the relationship between the quality of life 9 months after and the severity of the SARS-CoV-2 infection in two scenarios: hospitalization (with/without medical oxygen) and outpatient treatment.MethodsWe employed the EQ-5D-5L Quality of Life tests and the PSQI as a survey to evaluate respondents' quality of life 9 months after a previous SARS-CoV-2 infection of varying severity.ResultsWe identified a clear difference in the quality of life of respondents, as measured on the 100-point scale of the EQ-5D-5L test, which was significantly lower 9 months after a previous SARS-CoV-2 infection for Group 1 (n = 14), respondents who had received medical attention for SARS-CoV-2 infection in a hospital with oxygen treatment, compared to those with the SARS-CoV-2 infection who were treated without oxygen treatment (Group 2) (n = 12) and those who were treated on an outpatient basis (Group 3) (n = 13) (H = 7.08 p = 0.029). There were no intergroup differences in quality of life indicators between hospitalized patients (Group 2) and groups 1 and 3. PSQI survey results showed that “mobility,” “self-care,” “daily activities,” “pain/discomfort,” and “anxiety/ depression” did not differ significantly between the groups, indicating that these factors were not associated with the severity of the SARS-CoV-2 infection. On the contrary, the respondents demonstrated significant inter-group differences (H = 7.51 p = 0.023) and the interdependence of respiratory difficulties with the severity of clinically diagnosed SARS-CoV-2 infection. This study also demonstrated significant differences in the values of sleep duration, sleep disorders, and daytime sleepiness indicators between the three groups of respondents, which indicate the influence of the severity of the infection. The PSQI test results revealed significant differences in “bedtime” (H = 6.00 p = 0.050) and “wake-up time” (H = 11.17 p = 0.004) between Groups 1 and 3 of respondents. At 9 months after COVID-19, respondents in Group 1 went to bed at a later time (pp = 0.02727) and woke up later (p = 0.003) than the respondents in Group 3.ConclusionThis study is the first of its kind in the current literature to report on the quality of life of respondents 9 months after being diagnosed with COVID-19 and to draw comparisons between cohorts of hospitalized patients who were treated with medical oxygen vs. the cohorts of outpatient patients. The study's findings regarding post-COVID-19 quality of life indicators and their correlation with the severity of the SARS-CoV-2 infection can be used to categorize patients for targeted post-COVID-19 rehabilitation programs.
Muskan Shrivastava, Ritesh Chugh, Saikat Gochhait, and Abdul Bashiru Jibril IEEE
Digital twin technology is the union of three systems - the physical, the virtual and the interconnecting link layer. It acts as a replica and provides real-time representation of real-world physical objects in a virtual format. Digital twins are promising when applied in healthcare. This brief review gives a schematic overview of the current state of digital twin, specifically in the healthcare industry with gaps to overcome the challenges in building smart healthcare ecosystem. A concise synthesis of the following aspects of digital twin provides a cohesive whole in this paper: new capabilities and functions of the digital twin in healthcare, technologies that support digital twins, with its applications. This study motivated by the current COVID-19 pandemic, focuses on the application of the Digital Twin technology in healthcare.
Akash Deep and Saikat Gochhait IEEE
In today's society, most of the waking hours are spent on mobile phones and personal computing devices; as reliance on technology and online transactions grows, which in turn increases the security risk. S martphones are becoming more accessible and convenient due to rapid technological improvements and cost reductions. The increasing dependence has serious security ramifications, mainly if the users are uninformed of smartphone information security risks. This paper discusses various security threats, viruses, countermeasures, information gathering, and OS, emphasizing open-source software developed to safeguard mobile phones from these attacks. The ease with which cyber attackers may acquire access to personal data is through cell phones to do private tasks. As a result, recommended practices for protecting and securing smart mobile phones were presented in this study.
Trisa Modi and Saikat Gochhait IEEE
With expanding digital transformation of almost all sectors, deployment of different technologies to complement businesses has become ubiquitous. However, discussions regarding the use of technologies to enhance the effectiveness of other technologies has become ever important. This study is one of such attempts that explores the various ways in which gamification andgamified systems can be enhanced through the use of Machine Learning - based algorithms as well as the challenges faced dueto their convergence. More than 100 journal articles and web articles were explored to investigate the existing, upcoming and potential use cases for Artificial Intelligence (AI) and Machine Learning (ML) - based gamification. Multiple use cases and applications were found in various sectors such as e-commerce, ed-tech, medicine and many more. The study will be useful for researchers, technology developers and businesses to think, dis- cuss and develop innovative ways to deploy Artificial Intelligence- enabled gamification to leverage its benefits for themselves.
Saikat Gochhait and Amola Srivastava Springer Nature Singapore
Rodrick Wallace, Irina Leonova, and Saikat Gochhait MDPI AG
A central conundrum enshrouds biocognition: almost all such phenomena are inherently unstable and must be constantly controlled by external regulatory machinery to ensure proper function, in much the same sense that blood pressure and the ‘stream of consciousness’ require persistent delicate regulation for the survival of higher organisms. Here, we derive the Data Rate Theorem of control theory that characterizes such instability via the Rate Distortion Theorem of information theory for adiabatically stationary nonergodic systems. We then outline a novel approach to building new statistical tools for data analysis based on those theorems, focusing on groupoid symmetry-breaking phase transitions characterized by Fisher Zero analogs.
P. Keerthana and Saikat Gochhait Springer Nature Singapore
Saikar Gochhait, Ronak Asodiya, Totappa Hasarmani, Vasily Patin, and Olga Maslova IEEE
In the globe, there are two forms of energy: renewable and non-renewable. Every day, the amount of energy consumed rises. People are shifting to renewable energy as a result of carbon dioxide emissions. Renewable energy sources include solar, wind, and geothermal energy. The sun radiates solar energy, which is potential energy. Solar panels are used to produce power from solar energy. Solar plants are employed in a dusty climate, such as that seen in tropical nations like India. Dust on PV panels reduces the efficiency of the panels' ability to generate power by 50 percent. Cleaning solar panels is necessary because dust decreases the producing power of PV panels by blocking the intensity of light and raising the temperature of the panels. Human resources have traditionally cleaned panels. It's a time-consuming and costly job. Solar panels are also damaged during manual cleaning. The automated system can easily clean it. The automatic solar panel cleaning system has the potential to boost efficiency by 32 percent. PV panels may be cleaned with or without water using this system. With the use of automation and the Internet of Things, it can be fully automated. Cleaning using an automated system consumes less water and is more efficient. With the help of sensors, microcontrollers, and other devices, this machine can work both manually and automatically. It features a horizontal and vertical frame that allows it to clean a number of panels back and forwards. Data on generation and efficiency is monitored via systems. By cleaning the panels on a regular basis, it can increase efficiency by 1.6 to 2.2 percent. The many aspects of cleaning solar panels and the automated solar panel cleaning system are covered in this literature review.
Saikat Gochhait, Harish Patil, Totappa Hasarmani, Vasily Patin, and Olga Maslova IEEE
The Internet of Things is an evolving technology that effectively and efficiently enhances everyday activities. Reduce your living expenses by performing the necessary procedures. For synchronized communication, the tools and materials are integrated via the Internet. The price of electricity is continuously increasing, so we need natural sources of electricity that can generate power without cost. In this case, photovoltaic panels produce electricity naturally from the sun. Photovoltaic cells are used in these systems that convert sunlight into electricity. In the power sector, renewable energy sources are currently an exceptional solution for filling the gap in supply. Since solar energy is widely available, unlike some resources that are geographically restricted, it is very beneficial for all renewable energy resources. An advanced plant-based monitoring system coupled with a web-based visual interface is required for this large-scale PV system distribution. In most cases, they are placed in inaccessible places so self-monitoring is impossible in a particular area. Internet of Things (IoT) enabled devices can be used to build control systems. By using the Internet of Things, objects can be detected and controlled remotely over a fixed network, offering opportunities for virtually integrating physical objects into computer-based systems. The use of IoT appears to be beneficial in monitoring renewable energy production. An Arduino-based system is used in this IoT app for tracking the parameters of solar panels. The system continuously monitors the solar panel, and the output power is transmitted over the Internet to the IoT Network A solar photovoltaic power generation plant can be significantly enhanced by using Internet of Things technology for monitoring, analysing, and maintaining its performance. Technology advancements are making renewable energy equipment more affordable worldwide, encouraging the installation of large-scale solar photovoltaic systems. In this paper, an IoT-based approach to solar energy and monitoring is proposed that allows users to monitor and control their solar cells.
Department of Science and Industrial Research , Govt of India with Grant of Rs 13,000,00
Ministry of Foreign Affairs, Taiwan with Grant of Rs 12,000,00
University of Deusto, Spain with Research Grant of Rs 2,000,00
University of Extremadura, Spain with Research Grant of Rs 2,000,00
Samara State Medical University, Russia with Research Visit grant of Rs 2,500,00
Symbiosis International Deemed University with Travel and Research Grant of 4,000,000
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